Cosmic-Ray Drag and Damping of Compressive Turbulence

Author:

Bustard ChadORCID,Oh S. PengORCID

Abstract

Abstract While it is well known that cosmic rays (CRs) can gain energy from turbulence via second-order Fermi acceleration, how this energy transfer affects the turbulent cascade remains largely unexplored. Here, we show that damping and steepening of the compressive turbulent power spectrum are expected once the damping time t damp ρ v 2 / E ̇ CR E CR 1 becomes comparable to the turbulent cascade time. Magnetohydrodynamic simulations of stirred compressive turbulence in a gas-CR fluid with diffusive CR transport show clear imprints of CR-induced damping, saturating at E ̇ CR ϵ ˜ , where ϵ ˜ is the turbulent energy input rate. In that case, almost all of the energy in large-scale motions is absorbed by CRs and does not cascade down to grid scale. Through a Hodge–Helmholtz decomposition, we confirm that purely compressive forcing can generate significant solenoidal motions, and we find preferential CR damping of the compressive component in simulations with diffusion and streaming, rendering small-scale turbulence largely solenoidal, with implications for thermal instability and proposed resonant scattering of E ≳ 300 GeV CRs by fast modes. When CR transport is streaming dominated, CRs also damp large-scale motions, with kinetic energy reduced by up to 1 order of magnitude in realistic E CRE g scenarios, but turbulence (with a reduced amplitude) still cascades down to small scales with the same power spectrum. Such large-scale damping implies that turbulent velocities obtained from the observed velocity dispersion may significantly underestimate turbulent forcing rates, i.e., ϵ ˜ ρ v 3 / L .

Funder

National Science Foundation

Gordon and Betty Moore Foundation

NASA ∣ SMD ∣ Astrophysics Division

Publisher

American Astronomical Society

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep learning cosmic ray transport from density maps of simulated, turbulent gas;Machine Learning: Science and Technology;2024-02-14

2. Cosmic ray feedback in galaxies and galaxy clusters;The Astronomy and Astrophysics Review;2023-12

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